방법 비교

	엘라스틱 넷 회귀	최소제곱법(OLS) 회귀
분야	통계학	계량경제학
계열	Regression model	Regression model
기원 연도	2005	2019
창시자	Hui Zou and Trevor Hastie	Wooldridge (textbook treatment); classical least squares
유형	Penalized linear regression	Linear regression
원전	Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
별칭	elastic net, EN regression, L1+L2 regularized regression, combined lasso-ridge regression	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
관련	6	5
요약	Elastic net regression combines the L1 (lasso) and L2 (ridge) penalties into a single regularized regression framework. Controlled by a mixing parameter alpha and a shrinkage strength lambda, it can simultaneously select variables and handle correlated predictors — overcoming key limitations of pure lasso and pure ridge applied alone.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).